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<a href="#pub-types">Public 类型</a> &#124;
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pro-methods">Protected 成员函数</a> &#124;
<a href="#pro-attribs">Protected 属性</a> &#124;
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<div class="title">pcl::SACSegmentation&lt; PointT &gt; 模板类 参考</div>  </div>
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<p><b><a class="el" href="classpcl_1_1_s_a_c_segmentation.html" title="SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models,...">SACSegmentation</a></b> represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation.  
 <a href="classpcl_1_1_s_a_c_segmentation.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="sac__segmentation_8h_source.html">sac_segmentation.h</a>&gt;</code></p>
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类 pcl::SACSegmentation&lt; PointT &gt; 继承关系图:</div>
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  <img src="classpcl_1_1_s_a_c_segmentation.png" usemap="#pcl::SACSegmentation_3C_20PointT_20_3E_map" alt=""/>
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<table class="memberdecls">
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Public 类型</h2></td></tr>
<tr class="memitem:aa09fbf509ac114962b171cfd14a5dc45"><td class="memItemLeft" align="right" valign="top"><a id="aa09fbf509ac114962b171cfd14a5dc45"></a>
typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloud</b></td></tr>
<tr class="separator:aa09fbf509ac114962b171cfd14a5dc45"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6059dcf8a4237ea31407f8dd2f09caed"><td class="memItemLeft" align="right" valign="top"><a id="a6059dcf8a4237ea31407f8dd2f09caed"></a>
typedef PointCloud::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudPtr</b></td></tr>
<tr class="separator:a6059dcf8a4237ea31407f8dd2f09caed"><td class="memSeparator" colspan="2">&#160;</td></tr>
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typedef PointCloud::ConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudConstPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1search_1_1_search.html">pcl::search::Search</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>SearchPtr</b></td></tr>
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<tr class="memitem:a690cd433d894b90418e00f7d5867cc07"><td class="memItemLeft" align="right" valign="top"><a id="a690cd433d894b90418e00f7d5867cc07"></a>
typedef <a class="el" href="classpcl_1_1_sample_consensus.html">SampleConsensus</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>SampleConsensusPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_sample_consensus_model.html">SampleConsensusModel</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>SampleConsensusModelPtr</b></td></tr>
<tr class="separator:ad0cfee2da069f7bddef93b279b3a61d6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_types_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pub_types_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Public 类型 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointT &gt;</a></td></tr>
<tr class="memitem:ae2f6f6863a73337858b7a7a054eaae4f inherit pub_types_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="ae2f6f6863a73337858b7a7a054eaae4f"></a>
typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloud</b></td></tr>
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typedef PointCloud::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudPtr</b></td></tr>
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typedef PointCloud::ConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudConstPtr</b></td></tr>
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typedef boost::shared_ptr&lt; <a class="el" href="structpcl_1_1_point_indices.html">PointIndices</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointIndicesPtr</b></td></tr>
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<tr class="memitem:a51771056fb4ab8c448a11157acbe2ee0 inherit pub_types_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a51771056fb4ab8c448a11157acbe2ee0"></a>
typedef boost::shared_ptr&lt; <a class="el" href="structpcl_1_1_point_indices.html">PointIndices</a> const  &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointIndicesConstPtr</b></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:addeabe3a311db251ef7d0a25879f1f4a"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#addeabe3a311db251ef7d0a25879f1f4a">SACSegmentation</a> (bool random=false)</td></tr>
<tr class="memdesc:addeabe3a311db251ef7d0a25879f1f4a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty constructor.  <a href="classpcl_1_1_s_a_c_segmentation.html#addeabe3a311db251ef7d0a25879f1f4a">更多...</a><br /></td></tr>
<tr class="separator:addeabe3a311db251ef7d0a25879f1f4a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0a6a501237dea40ae68c62f364e99cdf"><td class="memItemLeft" align="right" valign="top"><a id="a0a6a501237dea40ae68c62f364e99cdf"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a0a6a501237dea40ae68c62f364e99cdf">~SACSegmentation</a> ()</td></tr>
<tr class="memdesc:a0a6a501237dea40ae68c62f364e99cdf"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty destructor. <br /></td></tr>
<tr class="separator:a0a6a501237dea40ae68c62f364e99cdf"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaf3488729fa23a602cc0ef2e9480c5f5"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#aaf3488729fa23a602cc0ef2e9480c5f5">setModelType</a> (int model)</td></tr>
<tr class="memdesc:aaf3488729fa23a602cc0ef2e9480c5f5"><td class="mdescLeft">&#160;</td><td class="mdescRight">The type of model to use (user given parameter).  <a href="classpcl_1_1_s_a_c_segmentation.html#aaf3488729fa23a602cc0ef2e9480c5f5">更多...</a><br /></td></tr>
<tr class="separator:aaf3488729fa23a602cc0ef2e9480c5f5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa6ca63b1a3068f27ad7c9aeae462dfe2"><td class="memItemLeft" align="right" valign="top"><a id="aa6ca63b1a3068f27ad7c9aeae462dfe2"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#aa6ca63b1a3068f27ad7c9aeae462dfe2">getModelType</a> () const</td></tr>
<tr class="memdesc:aa6ca63b1a3068f27ad7c9aeae462dfe2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the type of SAC model used. <br /></td></tr>
<tr class="separator:aa6ca63b1a3068f27ad7c9aeae462dfe2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5fde989b5cec51bbec33cb0973186fe2"><td class="memItemLeft" align="right" valign="top"><a id="a5fde989b5cec51bbec33cb0973186fe2"></a>
SampleConsensusPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a5fde989b5cec51bbec33cb0973186fe2">getMethod</a> () const</td></tr>
<tr class="memdesc:a5fde989b5cec51bbec33cb0973186fe2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the SAC method used. <br /></td></tr>
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<tr class="memitem:ac7b9564ceba35754837b4848cf448d78"><td class="memItemLeft" align="right" valign="top"><a id="ac7b9564ceba35754837b4848cf448d78"></a>
SampleConsensusModelPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#ac7b9564ceba35754837b4848cf448d78">getModel</a> () const</td></tr>
<tr class="memdesc:ac7b9564ceba35754837b4848cf448d78"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the SAC model used. <br /></td></tr>
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<tr class="memitem:a1f01af4b5cc22e916c4facc145bc9297"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a1f01af4b5cc22e916c4facc145bc9297">setMethodType</a> (int method)</td></tr>
<tr class="memdesc:a1f01af4b5cc22e916c4facc145bc9297"><td class="mdescLeft">&#160;</td><td class="mdescRight">The type of sample consensus method to use (user given parameter).  <a href="classpcl_1_1_s_a_c_segmentation.html#a1f01af4b5cc22e916c4facc145bc9297">更多...</a><br /></td></tr>
<tr class="separator:a1f01af4b5cc22e916c4facc145bc9297"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a063b9f3f9beebfe252ef206dfa8fb6f5"><td class="memItemLeft" align="right" valign="top"><a id="a063b9f3f9beebfe252ef206dfa8fb6f5"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a063b9f3f9beebfe252ef206dfa8fb6f5">getMethodType</a> () const</td></tr>
<tr class="memdesc:a063b9f3f9beebfe252ef206dfa8fb6f5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the type of sample consensus method used. <br /></td></tr>
<tr class="separator:a063b9f3f9beebfe252ef206dfa8fb6f5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab303bdf338af51e757095fdcdd7dcf5a"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#ab303bdf338af51e757095fdcdd7dcf5a">setDistanceThreshold</a> (double threshold)</td></tr>
<tr class="memdesc:ab303bdf338af51e757095fdcdd7dcf5a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Distance to the model threshold (user given parameter).  <a href="classpcl_1_1_s_a_c_segmentation.html#ab303bdf338af51e757095fdcdd7dcf5a">更多...</a><br /></td></tr>
<tr class="separator:ab303bdf338af51e757095fdcdd7dcf5a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a37153f3267f908eb64719749dd9a5428"><td class="memItemLeft" align="right" valign="top"><a id="a37153f3267f908eb64719749dd9a5428"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a37153f3267f908eb64719749dd9a5428">getDistanceThreshold</a> () const</td></tr>
<tr class="memdesc:a37153f3267f908eb64719749dd9a5428"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the distance to the model threshold. <br /></td></tr>
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<tr class="memitem:a47c5241af3824ee197e3a9c1b89806c4"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a47c5241af3824ee197e3a9c1b89806c4">setMaxIterations</a> (int max_iterations)</td></tr>
<tr class="memdesc:a47c5241af3824ee197e3a9c1b89806c4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the maximum number of iterations before giving up.  <a href="classpcl_1_1_s_a_c_segmentation.html#a47c5241af3824ee197e3a9c1b89806c4">更多...</a><br /></td></tr>
<tr class="separator:a47c5241af3824ee197e3a9c1b89806c4"><td class="memSeparator" colspan="2">&#160;</td></tr>
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int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#adad96aff89dd14bd02ae5790b701e519">getMaxIterations</a> () const</td></tr>
<tr class="memdesc:adad96aff89dd14bd02ae5790b701e519"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get maximum number of iterations before giving up. <br /></td></tr>
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<tr class="memitem:a6c436f52078056b626aba0c36b819235"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a6c436f52078056b626aba0c36b819235">setProbability</a> (double probability)</td></tr>
<tr class="memdesc:a6c436f52078056b626aba0c36b819235"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the probability of choosing at least one sample free from outliers.  <a href="classpcl_1_1_s_a_c_segmentation.html#a6c436f52078056b626aba0c36b819235">更多...</a><br /></td></tr>
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<tr class="memitem:a41f0f4101d35fcffd17923204989fbf0"><td class="memItemLeft" align="right" valign="top"><a id="a41f0f4101d35fcffd17923204989fbf0"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a41f0f4101d35fcffd17923204989fbf0">getProbability</a> () const</td></tr>
<tr class="memdesc:a41f0f4101d35fcffd17923204989fbf0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the probability of choosing at least one sample free from outliers. <br /></td></tr>
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<tr class="memitem:acd7cb38442e52a3df81bc0fd28c07646"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#acd7cb38442e52a3df81bc0fd28c07646">setOptimizeCoefficients</a> (bool optimize)</td></tr>
<tr class="memdesc:acd7cb38442e52a3df81bc0fd28c07646"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set to true if a coefficient refinement is required.  <a href="classpcl_1_1_s_a_c_segmentation.html#acd7cb38442e52a3df81bc0fd28c07646">更多...</a><br /></td></tr>
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<tr class="memitem:a5f2152f5157113171a2d0a077ad17731"><td class="memItemLeft" align="right" valign="top"><a id="a5f2152f5157113171a2d0a077ad17731"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a5f2152f5157113171a2d0a077ad17731">getOptimizeCoefficients</a> () const</td></tr>
<tr class="memdesc:a5f2152f5157113171a2d0a077ad17731"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the coefficient refinement internal flag. <br /></td></tr>
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<tr class="memitem:a18ba57a99fbbcb0f2d0500b0d21579d2"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a18ba57a99fbbcb0f2d0500b0d21579d2">setRadiusLimits</a> (const double &amp;min_radius, const double &amp;max_radius)</td></tr>
<tr class="memdesc:a18ba57a99fbbcb0f2d0500b0d21579d2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the minimum and maximum allowable radius limits for the model (applicable to models that estimate a radius)  <a href="classpcl_1_1_s_a_c_segmentation.html#a18ba57a99fbbcb0f2d0500b0d21579d2">更多...</a><br /></td></tr>
<tr class="separator:a18ba57a99fbbcb0f2d0500b0d21579d2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae02b4d2a62f5dc94deb71f3412b93916"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#ae02b4d2a62f5dc94deb71f3412b93916">getRadiusLimits</a> (double &amp;min_radius, double &amp;max_radius)</td></tr>
<tr class="memdesc:ae02b4d2a62f5dc94deb71f3412b93916"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the minimum and maximum allowable radius limits for the model as set by the user.  <a href="classpcl_1_1_s_a_c_segmentation.html#ae02b4d2a62f5dc94deb71f3412b93916">更多...</a><br /></td></tr>
<tr class="separator:ae02b4d2a62f5dc94deb71f3412b93916"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac8a399afca15a3e2dcbf491a8de3e2f1"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#ac8a399afca15a3e2dcbf491a8de3e2f1">setSamplesMaxDist</a> (const double &amp;radius, SearchPtr search)</td></tr>
<tr class="memdesc:ac8a399afca15a3e2dcbf491a8de3e2f1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the maximum distance allowed when drawing random samples  <a href="classpcl_1_1_s_a_c_segmentation.html#ac8a399afca15a3e2dcbf491a8de3e2f1">更多...</a><br /></td></tr>
<tr class="separator:ac8a399afca15a3e2dcbf491a8de3e2f1"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a968404675723ac44d5cb8fca689fd620"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a968404675723ac44d5cb8fca689fd620">getSamplesMaxDist</a> (double &amp;radius)</td></tr>
<tr class="memdesc:a968404675723ac44d5cb8fca689fd620"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get maximum distance allowed when drawing random samples  <a href="classpcl_1_1_s_a_c_segmentation.html#a968404675723ac44d5cb8fca689fd620">更多...</a><br /></td></tr>
<tr class="separator:a968404675723ac44d5cb8fca689fd620"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a23abc3e522ccb2b2846a6c9b0cf7b7d3"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a23abc3e522ccb2b2846a6c9b0cf7b7d3">setAxis</a> (const Eigen::Vector3f &amp;ax)</td></tr>
<tr class="memdesc:a23abc3e522ccb2b2846a6c9b0cf7b7d3"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the axis along which we need to search for a model perpendicular to.  <a href="classpcl_1_1_s_a_c_segmentation.html#a23abc3e522ccb2b2846a6c9b0cf7b7d3">更多...</a><br /></td></tr>
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<tr class="memitem:af521155221e948e151b726cd76495fd0"><td class="memItemLeft" align="right" valign="top"><a id="af521155221e948e151b726cd76495fd0"></a>
Eigen::Vector3f&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#af521155221e948e151b726cd76495fd0">getAxis</a> () const</td></tr>
<tr class="memdesc:af521155221e948e151b726cd76495fd0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the axis along which we need to search for a model perpendicular to. <br /></td></tr>
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<tr class="memitem:a7a2dc31039a1717f83ca281f6970eb18"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a7a2dc31039a1717f83ca281f6970eb18">setEpsAngle</a> (double ea)</td></tr>
<tr class="memdesc:a7a2dc31039a1717f83ca281f6970eb18"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the angle epsilon (delta) threshold.  <a href="classpcl_1_1_s_a_c_segmentation.html#a7a2dc31039a1717f83ca281f6970eb18">更多...</a><br /></td></tr>
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<tr class="memitem:a93f67b554b55b3f10170065713263910"><td class="memItemLeft" align="right" valign="top"><a id="a93f67b554b55b3f10170065713263910"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a93f67b554b55b3f10170065713263910">getEpsAngle</a> () const</td></tr>
<tr class="memdesc:a93f67b554b55b3f10170065713263910"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the epsilon (delta) model angle threshold in radians. <br /></td></tr>
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<tr class="memitem:a514043477fc0efc79aaae0b595cc566c"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a514043477fc0efc79aaae0b595cc566c">segment</a> (<a class="el" href="structpcl_1_1_point_indices.html">PointIndices</a> &amp;inliers, <a class="el" href="structpcl_1_1_model_coefficients.html">ModelCoefficients</a> &amp;model_coefficients)</td></tr>
<tr class="memdesc:a514043477fc0efc79aaae0b595cc566c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Base method for segmentation of a model in a <a class="el" href="classpcl_1_1_point_cloud.html" title="PointCloud represents the base class in PCL for storing collections of 3D points.">PointCloud</a> given by &lt;setInputCloud (), setIndices ()&gt;  <a href="classpcl_1_1_s_a_c_segmentation.html#a514043477fc0efc79aaae0b595cc566c">更多...</a><br /></td></tr>
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<tr class="inherit_header pub_methods_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Public 成员函数 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointT &gt;</a></td></tr>
<tr class="memitem:af4fbc5eb005057f8a0fc6d60bde595df inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="af4fbc5eb005057f8a0fc6d60bde595df"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#af4fbc5eb005057f8a0fc6d60bde595df">PCLBase</a> ()</td></tr>
<tr class="memdesc:af4fbc5eb005057f8a0fc6d60bde595df inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty constructor. <br /></td></tr>
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<tr class="memitem:a7a6dd7a91275d7737cf1b18005b47244 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a7a6dd7a91275d7737cf1b18005b47244"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a7a6dd7a91275d7737cf1b18005b47244">PCLBase</a> (const <a class="el" href="classpcl_1_1_p_c_l_base.html">PCLBase</a> &amp;base)</td></tr>
<tr class="memdesc:a7a6dd7a91275d7737cf1b18005b47244 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Copy constructor. <br /></td></tr>
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<tr class="memitem:ad5d6846e98e59c37dcc3dc9958d53966 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="ad5d6846e98e59c37dcc3dc9958d53966"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#ad5d6846e98e59c37dcc3dc9958d53966">~PCLBase</a> ()</td></tr>
<tr class="memdesc:ad5d6846e98e59c37dcc3dc9958d53966 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor. <br /></td></tr>
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<tr class="memitem:a1952d7101f3942bac3b69ed55c1ca7ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (const PointCloudConstPtr &amp;cloud)</td></tr>
<tr class="memdesc:a1952d7101f3942bac3b69ed55c1ca7ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the input dataset  <a href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">更多...</a><br /></td></tr>
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<tr class="memitem:a8cd745c4f7a792212f4fc3720b9d46ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a8cd745c4f7a792212f4fc3720b9d46ea"></a>
PointCloudConstPtr const&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a8cd745c4f7a792212f4fc3720b9d46ea">getInputCloud</a> () const</td></tr>
<tr class="memdesc:a8cd745c4f7a792212f4fc3720b9d46ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the input point cloud dataset. <br /></td></tr>
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<tr class="memitem:ab219359de6eb34c9d51e2e976dd1a0d1 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">setIndices</a> (const IndicesPtr &amp;indices)</td></tr>
<tr class="memdesc:ab219359de6eb34c9d51e2e976dd1a0d1 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the vector of indices that represents the input data.  <a href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">更多...</a><br /></td></tr>
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<tr class="memitem:a436c68c74b31e4dd00000adfbb11ca7c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a436c68c74b31e4dd00000adfbb11ca7c">setIndices</a> (const IndicesConstPtr &amp;indices)</td></tr>
<tr class="memdesc:a436c68c74b31e4dd00000adfbb11ca7c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the vector of indices that represents the input data.  <a href="classpcl_1_1_p_c_l_base.html#a436c68c74b31e4dd00000adfbb11ca7c">更多...</a><br /></td></tr>
<tr class="separator:a436c68c74b31e4dd00000adfbb11ca7c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af9cc90d8364ce968566f75800d3773ca inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#af9cc90d8364ce968566f75800d3773ca">setIndices</a> (const PointIndicesConstPtr &amp;indices)</td></tr>
<tr class="memdesc:af9cc90d8364ce968566f75800d3773ca inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the vector of indices that represents the input data.  <a href="classpcl_1_1_p_c_l_base.html#af9cc90d8364ce968566f75800d3773ca">更多...</a><br /></td></tr>
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<tr class="memitem:a930c7a6375fdf65ff8cfdb4eb4a6d996 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a930c7a6375fdf65ff8cfdb4eb4a6d996">setIndices</a> (size_t row_start, size_t col_start, size_t nb_rows, size_t nb_cols)</td></tr>
<tr class="memdesc:a930c7a6375fdf65ff8cfdb4eb4a6d996 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the indices for the points laying within an interest region of the point cloud.  <a href="classpcl_1_1_p_c_l_base.html#a930c7a6375fdf65ff8cfdb4eb4a6d996">更多...</a><br /></td></tr>
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<tr class="memitem:a058753dd4de73d3d0062fe2e452fba3c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a058753dd4de73d3d0062fe2e452fba3c"></a>
IndicesPtr const&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a058753dd4de73d3d0062fe2e452fba3c">getIndices</a> ()</td></tr>
<tr class="memdesc:a058753dd4de73d3d0062fe2e452fba3c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the vector of indices used. <br /></td></tr>
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<tr class="memitem:acae187b37230758959572ceb1e6e2045 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="acae187b37230758959572ceb1e6e2045"></a>
IndicesConstPtr const&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#acae187b37230758959572ceb1e6e2045">getIndices</a> () const</td></tr>
<tr class="memdesc:acae187b37230758959572ceb1e6e2045 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the vector of indices used. <br /></td></tr>
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<tr class="memitem:af7335fedb0af0930b9d1dedcb54ba201 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#af7335fedb0af0930b9d1dedcb54ba201">operator[]</a> (size_t pos) const</td></tr>
<tr class="memdesc:af7335fedb0af0930b9d1dedcb54ba201 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Override <a class="el" href="classpcl_1_1_point_cloud.html" title="PointCloud represents the base class in PCL for storing collections of 3D points.">PointCloud</a> operator[] to shorten code  <a href="classpcl_1_1_p_c_l_base.html#af7335fedb0af0930b9d1dedcb54ba201">更多...</a><br /></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-methods"></a>
Protected 成员函数</h2></td></tr>
<tr class="memitem:a73f671838c3ec8c0f08a3c78a7cac80b"><td class="memItemLeft" align="right" valign="top">virtual bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a73f671838c3ec8c0f08a3c78a7cac80b">initSACModel</a> (const int model_type)</td></tr>
<tr class="memdesc:a73f671838c3ec8c0f08a3c78a7cac80b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Initialize the Sample Consensus model and set its parameters.  <a href="classpcl_1_1_s_a_c_segmentation.html#a73f671838c3ec8c0f08a3c78a7cac80b">更多...</a><br /></td></tr>
<tr class="separator:a73f671838c3ec8c0f08a3c78a7cac80b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2524fe5506c0c43c425dbe492da133ed"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a2524fe5506c0c43c425dbe492da133ed">initSAC</a> (const int method_type)</td></tr>
<tr class="memdesc:a2524fe5506c0c43c425dbe492da133ed"><td class="mdescLeft">&#160;</td><td class="mdescRight">Initialize the Sample Consensus method and set its parameters.  <a href="classpcl_1_1_s_a_c_segmentation.html#a2524fe5506c0c43c425dbe492da133ed">更多...</a><br /></td></tr>
<tr class="separator:a2524fe5506c0c43c425dbe492da133ed"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a87f8b0013e358f2bffa6c44149bc2e66"><td class="memItemLeft" align="right" valign="top"><a id="a87f8b0013e358f2bffa6c44149bc2e66"></a>
virtual std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> () const</td></tr>
<tr class="memdesc:a87f8b0013e358f2bffa6c44149bc2e66"><td class="mdescLeft">&#160;</td><td class="mdescRight">Class get name method. <br /></td></tr>
<tr class="separator:a87f8b0013e358f2bffa6c44149bc2e66"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pro_methods_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Protected 成员函数 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointT &gt;</a></td></tr>
<tr class="memitem:acceb20854934f4cf77e266eb5a44d4f0 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">initCompute</a> ()</td></tr>
<tr class="memdesc:acceb20854934f4cf77e266eb5a44d4f0 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">This method should get called before starting the actual computation.  <a href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">更多...</a><br /></td></tr>
<tr class="separator:acceb20854934f4cf77e266eb5a44d4f0 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afc426c4eebb94b7734d4fa556bff1420 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="afc426c4eebb94b7734d4fa556bff1420"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">deinitCompute</a> ()</td></tr>
<tr class="memdesc:afc426c4eebb94b7734d4fa556bff1420 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">This method should get called after finishing the actual computation. <br /></td></tr>
<tr class="separator:afc426c4eebb94b7734d4fa556bff1420 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-attribs"></a>
Protected 属性</h2></td></tr>
<tr class="memitem:a196b024d56223dd122e0a1c23773acab"><td class="memItemLeft" align="right" valign="top"><a id="a196b024d56223dd122e0a1c23773acab"></a>
SampleConsensusModelPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a></td></tr>
<tr class="memdesc:a196b024d56223dd122e0a1c23773acab"><td class="mdescLeft">&#160;</td><td class="mdescRight">The model that needs to be segmented. <br /></td></tr>
<tr class="separator:a196b024d56223dd122e0a1c23773acab"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7e9ad0f4cd31e45c2ff03da17d0c9bce"><td class="memItemLeft" align="right" valign="top"><a id="a7e9ad0f4cd31e45c2ff03da17d0c9bce"></a>
SampleConsensusPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a></td></tr>
<tr class="memdesc:a7e9ad0f4cd31e45c2ff03da17d0c9bce"><td class="mdescLeft">&#160;</td><td class="mdescRight">The sample consensus segmentation method. <br /></td></tr>
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<tr class="memitem:a444d6de9dcabbe20ad1a152bea746dab"><td class="memItemLeft" align="right" valign="top"><a id="a444d6de9dcabbe20ad1a152bea746dab"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a444d6de9dcabbe20ad1a152bea746dab">model_type_</a></td></tr>
<tr class="memdesc:a444d6de9dcabbe20ad1a152bea746dab"><td class="mdescLeft">&#160;</td><td class="mdescRight">The type of model to use (user given parameter). <br /></td></tr>
<tr class="separator:a444d6de9dcabbe20ad1a152bea746dab"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af22b2d5e01d6c549b5d7c14caaa28539"><td class="memItemLeft" align="right" valign="top"><a id="af22b2d5e01d6c549b5d7c14caaa28539"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#af22b2d5e01d6c549b5d7c14caaa28539">method_type_</a></td></tr>
<tr class="memdesc:af22b2d5e01d6c549b5d7c14caaa28539"><td class="mdescLeft">&#160;</td><td class="mdescRight">The type of sample consensus method to use (user given parameter). <br /></td></tr>
<tr class="separator:af22b2d5e01d6c549b5d7c14caaa28539"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af262dd025eb187cfc6cb4e638562735a"><td class="memItemLeft" align="right" valign="top"><a id="af262dd025eb187cfc6cb4e638562735a"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a></td></tr>
<tr class="memdesc:af262dd025eb187cfc6cb4e638562735a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Distance to the model threshold (user given parameter). <br /></td></tr>
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<tr class="memitem:abe8902ffbc5fd352e38c08f8b9b3601b"><td class="memItemLeft" align="right" valign="top"><a id="abe8902ffbc5fd352e38c08f8b9b3601b"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#abe8902ffbc5fd352e38c08f8b9b3601b">optimize_coefficients_</a></td></tr>
<tr class="memdesc:abe8902ffbc5fd352e38c08f8b9b3601b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set to true if a coefficient refinement is required. <br /></td></tr>
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<tr class="memitem:a873c817931f20ad93869526584c67e22"><td class="memItemLeft" align="right" valign="top"><a id="a873c817931f20ad93869526584c67e22"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a></td></tr>
<tr class="memdesc:a873c817931f20ad93869526584c67e22"><td class="mdescLeft">&#160;</td><td class="mdescRight">The minimum and maximum radius limits for the model. Applicable to all models that estimate a radius. <br /></td></tr>
<tr class="separator:a873c817931f20ad93869526584c67e22"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1b78e575f1e6eb2b6f32f8c60e777343"><td class="memItemLeft" align="right" valign="top"><a id="a1b78e575f1e6eb2b6f32f8c60e777343"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><b>radius_max_</b></td></tr>
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<tr class="memitem:acfefedfd77b047203d70b9a73c1cbec8"><td class="memItemLeft" align="right" valign="top"><a id="acfefedfd77b047203d70b9a73c1cbec8"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#acfefedfd77b047203d70b9a73c1cbec8">samples_radius_</a></td></tr>
<tr class="memdesc:acfefedfd77b047203d70b9a73c1cbec8"><td class="mdescLeft">&#160;</td><td class="mdescRight">The maximum distance of subsequent samples from the first (radius search) <br /></td></tr>
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<tr class="memitem:a8decd0cf3fa904cd8d2530879a7c726b"><td class="memItemLeft" align="right" valign="top"><a id="a8decd0cf3fa904cd8d2530879a7c726b"></a>
SearchPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a8decd0cf3fa904cd8d2530879a7c726b">samples_radius_search_</a></td></tr>
<tr class="memdesc:a8decd0cf3fa904cd8d2530879a7c726b"><td class="mdescLeft">&#160;</td><td class="mdescRight">The search object for picking subsequent samples using radius search <br /></td></tr>
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<tr class="memitem:ac40b9cd690894f581d507dabbeb89c3c"><td class="memItemLeft" align="right" valign="top"><a id="ac40b9cd690894f581d507dabbeb89c3c"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a></td></tr>
<tr class="memdesc:ac40b9cd690894f581d507dabbeb89c3c"><td class="mdescLeft">&#160;</td><td class="mdescRight">The maximum allowed difference between the model normal and the given axis. <br /></td></tr>
<tr class="separator:ac40b9cd690894f581d507dabbeb89c3c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abc9924cedf7d0d013f860b8ad3054b69"><td class="memItemLeft" align="right" valign="top"><a id="abc9924cedf7d0d013f860b8ad3054b69"></a>
Eigen::Vector3f&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a></td></tr>
<tr class="memdesc:abc9924cedf7d0d013f860b8ad3054b69"><td class="mdescLeft">&#160;</td><td class="mdescRight">The axis along which we need to search for a model perpendicular to. <br /></td></tr>
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<tr class="memitem:af99af32608e22a07bc0d8e0cf92d20fb"><td class="memItemLeft" align="right" valign="top"><a id="af99af32608e22a07bc0d8e0cf92d20fb"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#af99af32608e22a07bc0d8e0cf92d20fb">max_iterations_</a></td></tr>
<tr class="memdesc:af99af32608e22a07bc0d8e0cf92d20fb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Maximum number of iterations before giving up (user given parameter). <br /></td></tr>
<tr class="separator:af99af32608e22a07bc0d8e0cf92d20fb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a29d30627faa3b625702191de4fb3da9b"><td class="memItemLeft" align="right" valign="top"><a id="a29d30627faa3b625702191de4fb3da9b"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a29d30627faa3b625702191de4fb3da9b">probability_</a></td></tr>
<tr class="memdesc:a29d30627faa3b625702191de4fb3da9b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Desired probability of choosing at least one sample free from outliers (user given parameter). <br /></td></tr>
<tr class="separator:a29d30627faa3b625702191de4fb3da9b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7e4799ad1df36ce3b56a8c592a67bbf2"><td class="memItemLeft" align="right" valign="top"><a id="a7e4799ad1df36ce3b56a8c592a67bbf2"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html#a7e4799ad1df36ce3b56a8c592a67bbf2">random_</a></td></tr>
<tr class="memdesc:a7e4799ad1df36ce3b56a8c592a67bbf2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set to true if we need a random seed. <br /></td></tr>
<tr class="separator:a7e4799ad1df36ce3b56a8c592a67bbf2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pro_attribs_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Protected 属性 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointT &gt;</a></td></tr>
<tr class="memitem:a09c70d8e06e3fb4f07903fe6f8d67869 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a09c70d8e06e3fb4f07903fe6f8d67869"></a>
PointCloudConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a></td></tr>
<tr class="memdesc:a09c70d8e06e3fb4f07903fe6f8d67869 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">The input point cloud dataset. <br /></td></tr>
<tr class="separator:a09c70d8e06e3fb4f07903fe6f8d67869 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaee847c8a517ebf365bad2cb182a6626 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="aaee847c8a517ebf365bad2cb182a6626"></a>
IndicesPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a></td></tr>
<tr class="memdesc:aaee847c8a517ebf365bad2cb182a6626 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">A pointer to the vector of point indices to use. <br /></td></tr>
<tr class="separator:aaee847c8a517ebf365bad2cb182a6626 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ada1eadb824d34ca9206a86343d9760bb inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="ada1eadb824d34ca9206a86343d9760bb"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#ada1eadb824d34ca9206a86343d9760bb">use_indices_</a></td></tr>
<tr class="memdesc:ada1eadb824d34ca9206a86343d9760bb inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set to true if point indices are used. <br /></td></tr>
<tr class="separator:ada1eadb824d34ca9206a86343d9760bb inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:adadb0299f144528020ed558af6879662 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="adadb0299f144528020ed558af6879662"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#adadb0299f144528020ed558af6879662">fake_indices_</a></td></tr>
<tr class="memdesc:adadb0299f144528020ed558af6879662 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">If no set of indices are given, we construct a set of fake indices that mimic the input <a class="el" href="classpcl_1_1_point_cloud.html" title="PointCloud represents the base class in PCL for storing collections of 3D points.">PointCloud</a>. <br /></td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;typename PointT&gt;<br />
class pcl::SACSegmentation&lt; PointT &gt;</h3>

<p><b><a class="el" href="classpcl_1_1_s_a_c_segmentation.html" title="SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models,...">SACSegmentation</a></b> represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation. </p>
<dl class="section author"><dt>作者</dt><dd>Radu Bogdan Rusu </dd></dl>
</div><h2 class="groupheader">构造及析构函数说明</h2>
<a id="addeabe3a311db251ef7d0a25879f1f4a"></a>
<h2 class="memtitle"><span class="permalink"><a href="#addeabe3a311db251ef7d0a25879f1f4a">&#9670;&nbsp;</a></span>SACSegmentation()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname"><a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::<a class="el" href="classpcl_1_1_s_a_c_segmentation.html">SACSegmentation</a> </td>
          <td>(</td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>random</em> = <code>false</code></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Empty constructor. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">random</td><td>if true set the random seed to the current time, else set to 12345 (default: false) </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;        : <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a> ()</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a> ()</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a444d6de9dcabbe20ad1a152bea746dab">model_type_</a> (-1)</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af22b2d5e01d6c549b5d7c14caaa28539">method_type_</a> (0)</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a> (0)</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abe8902ffbc5fd352e38c08f8b9b3601b">optimize_coefficients_</a> (<span class="keyword">true</span>)</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a> (-std::numeric_limits&lt;double&gt;::max ())</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;        , radius_max_ (std::numeric_limits&lt;double&gt;::max ())</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#acfefedfd77b047203d70b9a73c1cbec8">samples_radius_</a> (0.0)</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a8decd0cf3fa904cd8d2530879a7c726b">samples_radius_search_</a> ()</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a> (0.0)</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a> (Eigen::Vector3f::Zero ())</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af99af32608e22a07bc0d8e0cf92d20fb">max_iterations_</a> (50)</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a29d30627faa3b625702191de4fb3da9b">probability_</a> (0.99)</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;        , <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e4799ad1df36ce3b56a8c592a67bbf2">random_</a> (random)</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;      {</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_a196b024d56223dd122e0a1c23773acab"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">pcl::SACSegmentation::model_</a></div><div class="ttdeci">SampleConsensusModelPtr model_</div><div class="ttdoc">The model that needs to be segmented.</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:260</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_a29d30627faa3b625702191de4fb3da9b"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#a29d30627faa3b625702191de4fb3da9b">pcl::SACSegmentation::probability_</a></div><div class="ttdeci">double probability_</div><div class="ttdoc">Desired probability of choosing at least one sample free from outliers (user given parameter).</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:296</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_a444d6de9dcabbe20ad1a152bea746dab"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#a444d6de9dcabbe20ad1a152bea746dab">pcl::SACSegmentation::model_type_</a></div><div class="ttdeci">int model_type_</div><div class="ttdoc">The type of model to use (user given parameter).</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:266</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_a7e4799ad1df36ce3b56a8c592a67bbf2"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#a7e4799ad1df36ce3b56a8c592a67bbf2">pcl::SACSegmentation::random_</a></div><div class="ttdeci">bool random_</div><div class="ttdoc">Set to true if we need a random seed.</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:299</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_a7e9ad0f4cd31e45c2ff03da17d0c9bce"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">pcl::SACSegmentation::sac_</a></div><div class="ttdeci">SampleConsensusPtr sac_</div><div class="ttdoc">The sample consensus segmentation method.</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:263</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_a873c817931f20ad93869526584c67e22"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">pcl::SACSegmentation::radius_min_</a></div><div class="ttdeci">double radius_min_</div><div class="ttdoc">The minimum and maximum radius limits for the model. Applicable to all models that estimate a radius.</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:278</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_a8decd0cf3fa904cd8d2530879a7c726b"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#a8decd0cf3fa904cd8d2530879a7c726b">pcl::SACSegmentation::samples_radius_search_</a></div><div class="ttdeci">SearchPtr samples_radius_search_</div><div class="ttdoc">The search object for picking subsequent samples using radius search</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:284</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_abc9924cedf7d0d013f860b8ad3054b69"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">pcl::SACSegmentation::axis_</a></div><div class="ttdeci">Eigen::Vector3f axis_</div><div class="ttdoc">The axis along which we need to search for a model perpendicular to.</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:290</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_abe8902ffbc5fd352e38c08f8b9b3601b"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#abe8902ffbc5fd352e38c08f8b9b3601b">pcl::SACSegmentation::optimize_coefficients_</a></div><div class="ttdeci">bool optimize_coefficients_</div><div class="ttdoc">Set to true if a coefficient refinement is required.</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:275</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_ac40b9cd690894f581d507dabbeb89c3c"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">pcl::SACSegmentation::eps_angle_</a></div><div class="ttdeci">double eps_angle_</div><div class="ttdoc">The maximum allowed difference between the model normal and the given axis.</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:287</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_acfefedfd77b047203d70b9a73c1cbec8"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#acfefedfd77b047203d70b9a73c1cbec8">pcl::SACSegmentation::samples_radius_</a></div><div class="ttdeci">double samples_radius_</div><div class="ttdoc">The maximum distance of subsequent samples from the first (radius search)</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:281</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_af22b2d5e01d6c549b5d7c14caaa28539"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#af22b2d5e01d6c549b5d7c14caaa28539">pcl::SACSegmentation::method_type_</a></div><div class="ttdeci">int method_type_</div><div class="ttdoc">The type of sample consensus method to use (user given parameter).</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:269</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_af262dd025eb187cfc6cb4e638562735a"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">pcl::SACSegmentation::threshold_</a></div><div class="ttdeci">double threshold_</div><div class="ttdoc">Distance to the model threshold (user given parameter).</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:272</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_af99af32608e22a07bc0d8e0cf92d20fb"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#af99af32608e22a07bc0d8e0cf92d20fb">pcl::SACSegmentation::max_iterations_</a></div><div class="ttdeci">int max_iterations_</div><div class="ttdoc">Maximum number of iterations before giving up (user given parameter).</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:293</div></div>
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<h2 class="groupheader">成员函数说明</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#ae02b4d2a62f5dc94deb71f3412b93916">&#9670;&nbsp;</a></span>getRadiusLimits()</h2>

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<div class="memproto">
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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::getRadiusLimits </td>
          <td>(</td>
          <td class="paramtype">double &amp;&#160;</td>
          <td class="paramname"><em>min_radius</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">double &amp;&#160;</td>
          <td class="paramname"><em>max_radius</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
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<p>Get the minimum and maximum allowable radius limits for the model as set by the user. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[out]</td><td class="paramname">min_radius</td><td>the resultant minimum radius model </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">max_radius</td><td>the resultant maximum radius model </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;      {</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;        min_radius = <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a>;</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;        max_radius = radius_max_;</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a968404675723ac44d5cb8fca689fd620">&#9670;&nbsp;</a></span>getSamplesMaxDist()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::getSamplesMaxDist </td>
          <td>(</td>
          <td class="paramtype">double &amp;&#160;</td>
          <td class="paramname"><em>radius</em></td><td>)</td>
          <td></td>
        </tr>
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<p>Get maximum distance allowed when drawing random samples </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[out]</td><td class="paramname">radius</td><td>the maximum distance (L2 norm) </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;      {</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        radius = <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#acfefedfd77b047203d70b9a73c1cbec8">samples_radius_</a>;</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a2524fe5506c0c43c425dbe492da133ed">&#9670;&nbsp;</a></span>initSAC()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::initSAC </td>
          <td>(</td>
          <td class="paramtype">const int&#160;</td>
          <td class="paramname"><em>method_type</em></td><td>)</td>
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<p>Initialize the Sample Consensus method and set its parameters. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">method_type</td><td>the type of SAC method to be used </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;{</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>)</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>.reset ();</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;  <span class="comment">// Build the sample consensus method</span></div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;  <span class="keywordflow">switch</span> (method_type)</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;  {</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <span class="keywordflow">case</span> SAC_RANSAC:</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <span class="keywordflow">default</span>:</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    {</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSAC] Using a method of type: SAC_RANSAC with a model threshold of %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>);</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>.reset (<span class="keyword">new</span> RandomSampleConsensus&lt;PointT&gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>));</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    }</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    <span class="keywordflow">case</span> SAC_LMEDS:</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    {</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSAC] Using a method of type: SAC_LMEDS with a model threshold of %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>);</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>.reset (<span class="keyword">new</span> LeastMedianSquares&lt;PointT&gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>));</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    }</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;    <span class="keywordflow">case</span> SAC_MSAC:</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    {</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSAC] Using a method of type: SAC_MSAC with a model threshold of %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>);</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>.reset (<span class="keyword">new</span> MEstimatorSampleConsensus&lt;PointT&gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>));</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    }</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    <span class="keywordflow">case</span> SAC_RRANSAC:</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    {</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSAC] Using a method of type: SAC_RRANSAC with a model threshold of %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>);</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>.reset (<span class="keyword">new</span> RandomizedRandomSampleConsensus&lt;PointT&gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>));</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    }</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    <span class="keywordflow">case</span> SAC_RMSAC:</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    {</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSAC] Using a method of type: SAC_RMSAC with a model threshold of %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>);</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>.reset (<span class="keyword">new</span> RandomizedMEstimatorSampleConsensus&lt;PointT&gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>));</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    }</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    <span class="keywordflow">case</span> SAC_MLESAC:</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    {</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSAC] Using a method of type: SAC_MLESAC with a model threshold of %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>);</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>.reset (<span class="keyword">new</span> MaximumLikelihoodSampleConsensus&lt;PointT&gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>));</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    }</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    <span class="keywordflow">case</span> SAC_PROSAC:</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    {</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSAC] Using a method of type: SAC_PROSAC with a model threshold of %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>);</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>.reset (<span class="keyword">new</span> ProgressiveSampleConsensus&lt;PointT&gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>));</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    }</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;  }</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;  <span class="comment">// Set the Sample Consensus parameters if they are given/changed</span></div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>-&gt;getProbability () != <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a29d30627faa3b625702191de4fb3da9b">probability_</a>)</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;  {</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSAC] Setting the desired probability to %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a29d30627faa3b625702191de4fb3da9b">probability_</a>);</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>-&gt;setProbability (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a29d30627faa3b625702191de4fb3da9b">probability_</a>);</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;  }</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af99af32608e22a07bc0d8e0cf92d20fb">max_iterations_</a> != -1 &amp;&amp; <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>-&gt;getMaxIterations () != <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af99af32608e22a07bc0d8e0cf92d20fb">max_iterations_</a>)</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;  {</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSAC] Setting the maximum number of iterations to %d\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af99af32608e22a07bc0d8e0cf92d20fb">max_iterations_</a>);</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>-&gt;setMaxIterations (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af99af32608e22a07bc0d8e0cf92d20fb">max_iterations_</a>);</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;  }</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#acfefedfd77b047203d70b9a73c1cbec8">samples_radius_</a> &gt; 0.)</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;  {</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSAC] Setting the maximum sample radius to %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#acfefedfd77b047203d70b9a73c1cbec8">samples_radius_</a>);</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    <span class="comment">// Set maximum distance for radius search during random sampling</span></div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>-&gt;setSamplesMaxDist (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#acfefedfd77b047203d70b9a73c1cbec8">samples_radius_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a8decd0cf3fa904cd8d2530879a7c726b">samples_radius_search_</a>);</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;  }</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_a87f8b0013e358f2bffa6c44149bc2e66"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">pcl::SACSegmentation::getClassName</a></div><div class="ttdeci">virtual std::string getClassName() const</div><div class="ttdoc">Class get name method.</div><div class="ttdef"><b>Definition:</b> sac_segmentation.h:303</div></div>
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<a id="a73f671838c3ec8c0f08a3c78a7cac80b"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a73f671838c3ec8c0f08a3c78a7cac80b">&#9670;&nbsp;</a></span>initSACModel()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">bool <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::initSACModel </td>
          <td>(</td>
          <td class="paramtype">const int&#160;</td>
          <td class="paramname"><em>model_type</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">protected</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Initialize the Sample Consensus model and set its parameters. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">model_type</td><td>the type of SAC model that is to be used </td></tr>
  </table>
  </dd>
</dl>

<p>被 <a class="el" href="classpcl_1_1_s_a_c_segmentation_from_normals.html#ac4d61a5778c41a40192d2e44729502a8">pcl::SACSegmentationFromNormals&lt; PointT, PointNT &gt;</a> , 以及 <a class="el" href="classpcl_1_1_s_a_c_segmentation_from_normals.html#ac4d61a5778c41a40192d2e44729502a8">pcl::SACSegmentationFromNormals&lt; PointType, pcl::Normal &gt;</a> 重载.</p>
<div class="fragment"><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;{</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>)</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>.reset ();</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160; </div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;  <span class="comment">// Build the model</span></div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;  <span class="keywordflow">switch</span> (model_type)</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;  {</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    <span class="keywordflow">case</span> SACMODEL_PLANE:</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    {</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Using a model of type: SACMODEL_PLANE\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>.reset (<span class="keyword">new</span> SampleConsensusModelPlane&lt;PointT&gt; (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>, *<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e4799ad1df36ce3b56a8c592a67bbf2">random_</a>));</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    }</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    <span class="keywordflow">case</span> SACMODEL_LINE:</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    {</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Using a model of type: SACMODEL_LINE\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>.reset (<span class="keyword">new</span> SampleConsensusModelLine&lt;PointT&gt; (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>, *<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e4799ad1df36ce3b56a8c592a67bbf2">random_</a>));</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;    }</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    <span class="keywordflow">case</span> SACMODEL_STICK:</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    {</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Using a model of type: SACMODEL_STICK\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>.reset (<span class="keyword">new</span> SampleConsensusModelStick&lt;PointT&gt; (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>, *<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>));</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;      <span class="keywordtype">double</span> min_radius, max_radius;</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>-&gt;getRadiusLimits (min_radius, max_radius);</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a> != min_radius &amp;&amp; radius_max_ != max_radius)</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;      {</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;        PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Setting radius limits to %f/%f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a>, radius_max_);</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;        <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>-&gt;setRadiusLimits (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a>, radius_max_);</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;      }</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    }</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    <span class="keywordflow">case</span> SACMODEL_CIRCLE2D:</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    {</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Using a model of type: SACMODEL_CIRCLE2D\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>.reset (<span class="keyword">new</span> SampleConsensusModelCircle2D&lt;PointT&gt; (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>, *<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e4799ad1df36ce3b56a8c592a67bbf2">random_</a>));</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;      <span class="keyword">typename</span> SampleConsensusModelCircle2D&lt;PointT&gt;::Ptr model_circle = boost::static_pointer_cast&lt;SampleConsensusModelCircle2D&lt;PointT&gt; &gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>);</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;      <span class="keywordtype">double</span> min_radius, max_radius;</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;      model_circle-&gt;getRadiusLimits (min_radius, max_radius);</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a> != min_radius &amp;&amp; radius_max_ != max_radius)</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;      {</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;        PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Setting radius limits to %f/%f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a>, radius_max_);</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;        model_circle-&gt;setRadiusLimits (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a>, radius_max_);</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;      }</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    }</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    <span class="keywordflow">case</span> SACMODEL_CIRCLE3D:</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    {</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Using a model of type: SACMODEL_CIRCLE3D\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>.reset (<span class="keyword">new</span> SampleConsensusModelCircle3D&lt;PointT&gt; (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>, *<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>));</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;      <span class="keyword">typename</span> SampleConsensusModelCircle3D&lt;PointT&gt;::Ptr model_circle3d = boost::static_pointer_cast&lt;SampleConsensusModelCircle3D&lt;PointT&gt; &gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>);</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      <span class="keywordtype">double</span> min_radius, max_radius;</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;      model_circle3d-&gt;getRadiusLimits (min_radius, max_radius);</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a> != min_radius &amp;&amp; radius_max_ != max_radius)</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;      {</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Setting radius limits to %f/%f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a>, radius_max_);</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;        model_circle3d-&gt;setRadiusLimits (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a>, radius_max_);</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;      }</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    }</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;    <span class="keywordflow">case</span> SACMODEL_SPHERE:</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    {</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Using a model of type: SACMODEL_SPHERE\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>.reset (<span class="keyword">new</span> SampleConsensusModelSphere&lt;PointT&gt; (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>, *<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e4799ad1df36ce3b56a8c592a67bbf2">random_</a>));</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;      <span class="keyword">typename</span> SampleConsensusModelSphere&lt;PointT&gt;::Ptr model_sphere = boost::static_pointer_cast&lt;SampleConsensusModelSphere&lt;PointT&gt; &gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>);</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;      <span class="keywordtype">double</span> min_radius, max_radius;</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;      model_sphere-&gt;getRadiusLimits (min_radius, max_radius);</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a> != min_radius &amp;&amp; radius_max_ != max_radius)</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;      {</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;        PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Setting radius limits to %f/%f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a>, radius_max_);</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        model_sphere-&gt;setRadiusLimits (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a>, radius_max_);</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;      }</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    }</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    <span class="keywordflow">case</span> SACMODEL_PARALLEL_LINE:</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;    {</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Using a model of type: SACMODEL_PARALLEL_LINE\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>.reset (<span class="keyword">new</span> SampleConsensusModelParallelLine&lt;PointT&gt; (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>, *<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e4799ad1df36ce3b56a8c592a67bbf2">random_</a>));</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;      <span class="keyword">typename</span> SampleConsensusModelParallelLine&lt;PointT&gt;::Ptr model_parallel = boost::static_pointer_cast&lt;SampleConsensusModelParallelLine&lt;PointT&gt; &gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>);</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a> != Eigen::Vector3f::Zero () &amp;&amp; model_parallel-&gt;getAxis () != <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>)</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;      {</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;        PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Setting the axis to %f, %f, %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>[0], <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>[1], <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>[2]);</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        model_parallel-&gt;setAxis (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>);</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;      }</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a> != 0.0 &amp;&amp; model_parallel-&gt;getEpsAngle () != <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a>)</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;      {</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;        PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Setting the epsilon angle to %f (%f degrees)\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a> * 180.0 / M_PI);</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        model_parallel-&gt;setEpsAngle (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a>);</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;      }</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;    }</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    <span class="keywordflow">case</span> SACMODEL_PERPENDICULAR_PLANE:</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    {</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Using a model of type: SACMODEL_PERPENDICULAR_PLANE\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>.reset (<span class="keyword">new</span> SampleConsensusModelPerpendicularPlane&lt;PointT&gt; (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>, *<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e4799ad1df36ce3b56a8c592a67bbf2">random_</a>));</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;      <span class="keyword">typename</span> SampleConsensusModelPerpendicularPlane&lt;PointT&gt;::Ptr model_perpendicular = boost::static_pointer_cast&lt;SampleConsensusModelPerpendicularPlane&lt;PointT&gt; &gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>);</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a> != Eigen::Vector3f::Zero () &amp;&amp; model_perpendicular-&gt;getAxis () != <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>)</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;      {</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Setting the axis to %f, %f, %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>[0], <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>[1], <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>[2]);</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;        model_perpendicular-&gt;setAxis (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>);</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;      }</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a> != 0.0 &amp;&amp; model_perpendicular-&gt;getEpsAngle () != <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a>)</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;      {</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;        PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Setting the epsilon angle to %f (%f degrees)\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a> * 180.0 / M_PI);</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;        model_perpendicular-&gt;setEpsAngle (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a>);</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;      }</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;    }</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    <span class="keywordflow">case</span> SACMODEL_PARALLEL_PLANE:</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    {</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Using a model of type: SACMODEL_PARALLEL_PLANE\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;      <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>.reset (<span class="keyword">new</span> SampleConsensusModelParallelPlane&lt;PointT&gt; (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>, *<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e4799ad1df36ce3b56a8c592a67bbf2">random_</a>));</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;      <span class="keyword">typename</span> SampleConsensusModelParallelPlane&lt;PointT&gt;::Ptr model_parallel = boost::static_pointer_cast&lt;SampleConsensusModelParallelPlane&lt;PointT&gt; &gt; (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>);</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a> != Eigen::Vector3f::Zero () &amp;&amp; model_parallel-&gt;getAxis () != <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>)</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;      {</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Setting the axis to %f, %f, %f\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>[0], <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>[1], <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>[2]);</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;        model_parallel-&gt;setAxis (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a>);</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;      }</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a> != 0.0 &amp;&amp; model_parallel-&gt;getEpsAngle () != <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a>)</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;      {</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        PCL_DEBUG (<span class="stringliteral">&quot;[pcl::%s::initSACModel] Setting the epsilon angle to %f (%f degrees)\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str (), <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a> * 180.0 / M_PI);</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        model_parallel-&gt;setEpsAngle (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a>);</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;      }</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    }</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    <span class="keywordflow">default</span>:</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;    {</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;      PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::initSACModel] No valid model given!\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;      <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    }</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;  }</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;  <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a09c70d8e06e3fb4f07903fe6f8d67869"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">pcl::PCLBase::input_</a></div><div class="ttdeci">PointCloudConstPtr input_</div><div class="ttdoc">The input point cloud dataset.</div><div class="ttdef"><b>Definition:</b> pcl_base.h:150</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_aaee847c8a517ebf365bad2cb182a6626"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">pcl::PCLBase::indices_</a></div><div class="ttdeci">IndicesPtr indices_</div><div class="ttdoc">A pointer to the vector of point indices to use.</div><div class="ttdef"><b>Definition:</b> pcl_base.h:153</div></div>
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<a id="a514043477fc0efc79aaae0b595cc566c"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a514043477fc0efc79aaae0b595cc566c">&#9670;&nbsp;</a></span>segment()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::segment </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="structpcl_1_1_point_indices.html">PointIndices</a> &amp;&#160;</td>
          <td class="paramname"><em>inliers</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="structpcl_1_1_model_coefficients.html">ModelCoefficients</a> &amp;&#160;</td>
          <td class="paramname"><em>model_coefficients</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Base method for segmentation of a model in a <a class="el" href="classpcl_1_1_point_cloud.html" title="PointCloud represents the base class in PCL for storing collections of 3D points.">PointCloud</a> given by &lt;setInputCloud (), setIndices ()&gt; </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[out]</td><td class="paramname">inliers</td><td>the resultant point indices that support the model found (inliers) </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">model_coefficients</td><td>the resultant model coefficients </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;{</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;  <span class="comment">// Copy the header information</span></div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;  inliers.header = model_coefficients.header = <a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;header;</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160; </div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">initCompute</a> ()) </div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;  {</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    inliers.indices.clear (); model_coefficients.values.clear ();</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;  }</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160; </div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;  <span class="comment">// Initialize the Sample Consensus model and set its parameters</span></div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a73f671838c3ec8c0f08a3c78a7cac80b">initSACModel</a> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a444d6de9dcabbe20ad1a152bea746dab">model_type_</a>))</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  {</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::segment] Error initializing the SAC model!\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    <a class="code" href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">deinitCompute</a> ();</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    inliers.indices.clear (); model_coefficients.values.clear ();</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;  }</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;  <span class="comment">// Initialize the Sample Consensus method and set its parameters</span></div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;  <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a2524fe5506c0c43c425dbe492da133ed">initSAC</a> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af22b2d5e01d6c549b5d7c14caaa28539">method_type_</a>);</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160; </div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>-&gt;computeModel (0))</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  {</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::segment] Error segmenting the model! No solution found.\n&quot;</span>, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a87f8b0013e358f2bffa6c44149bc2e66">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    <a class="code" href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">deinitCompute</a> ();</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    inliers.indices.clear (); model_coefficients.values.clear ();</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;  }</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160; </div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;  <span class="comment">// Get the model inliers</span></div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;  <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>-&gt;getInliers (inliers.indices);</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;  <span class="comment">// Get the model coefficients</span></div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;  Eigen::VectorXf coeff;</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;  <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a7e9ad0f4cd31e45c2ff03da17d0c9bce">sac_</a>-&gt;getModelCoefficients (coeff);</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160; </div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;  <span class="comment">// If the user needs optimized coefficients</span></div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abe8902ffbc5fd352e38c08f8b9b3601b">optimize_coefficients_</a>)</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;  {</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    Eigen::VectorXf coeff_refined;</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>-&gt;optimizeModelCoefficients (inliers.indices, coeff, coeff_refined);</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    model_coefficients.values.resize (coeff_refined.size ());</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    memcpy (&amp;model_coefficients.values[0], &amp;coeff_refined[0], coeff_refined.size () * sizeof (<span class="keywordtype">float</span>));</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <span class="comment">// Refine inliers</span></div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a196b024d56223dd122e0a1c23773acab">model_</a>-&gt;selectWithinDistance (coeff_refined, <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a>, inliers.indices);</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;  }</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;  {</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    model_coefficients.values.resize (coeff.size ());</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    memcpy (&amp;model_coefficients.values[0], &amp;coeff[0], coeff.size () * sizeof (<span class="keywordtype">float</span>));</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;  }</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160; </div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;  <a class="code" href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">deinitCompute</a> ();</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_acceb20854934f4cf77e266eb5a44d4f0"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">pcl::PCLBase::initCompute</a></div><div class="ttdeci">bool initCompute()</div><div class="ttdoc">This method should get called before starting the actual computation.</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:139</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_afc426c4eebb94b7734d4fa556bff1420"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">pcl::PCLBase::deinitCompute</a></div><div class="ttdeci">bool deinitCompute()</div><div class="ttdoc">This method should get called after finishing the actual computation.</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:174</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_a2524fe5506c0c43c425dbe492da133ed"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#a2524fe5506c0c43c425dbe492da133ed">pcl::SACSegmentation::initSAC</a></div><div class="ttdeci">virtual void initSAC(const int method_type)</div><div class="ttdoc">Initialize the Sample Consensus method and set its parameters.</div><div class="ttdef"><b>Definition:</b> sac_segmentation.hpp:270</div></div>
<div class="ttc" id="aclasspcl_1_1_s_a_c_segmentation_html_a73f671838c3ec8c0f08a3c78a7cac80b"><div class="ttname"><a href="classpcl_1_1_s_a_c_segmentation.html#a73f671838c3ec8c0f08a3c78a7cac80b">pcl::SACSegmentation::initSACModel</a></div><div class="ttdeci">virtual bool initSACModel(const int model_type)</div><div class="ttdoc">Initialize the Sample Consensus model and set its parameters.</div><div class="ttdef"><b>Definition:</b> sac_segmentation.hpp:133</div></div>
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<a id="a23abc3e522ccb2b2846a6c9b0cf7b7d3"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a23abc3e522ccb2b2846a6c9b0cf7b7d3">&#9670;&nbsp;</a></span>setAxis()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setAxis </td>
          <td>(</td>
          <td class="paramtype">const Eigen::Vector3f &amp;&#160;</td>
          <td class="paramname"><em>ax</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Set the axis along which we need to search for a model perpendicular to. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">ax</td><td>the axis along which we need to search for a model perpendicular to </td></tr>
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<div class="fragment"><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;{ <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abc9924cedf7d0d013f860b8ad3054b69">axis_</a> = ax; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ab303bdf338af51e757095fdcdd7dcf5a">&#9670;&nbsp;</a></span>setDistanceThreshold()</h2>

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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setDistanceThreshold </td>
          <td>(</td>
          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>threshold</em></td><td>)</td>
          <td></td>
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<p>Distance to the model threshold (user given parameter). </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">threshold</td><td>the distance threshold to use </td></tr>
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<div class="fragment"><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;{ <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af262dd025eb187cfc6cb4e638562735a">threshold_</a> = threshold; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a7a2dc31039a1717f83ca281f6970eb18">&#9670;&nbsp;</a></span>setEpsAngle()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setEpsAngle </td>
          <td>(</td>
          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>ea</em></td><td>)</td>
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<p>Set the angle epsilon (delta) threshold. </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">ea</td><td>the maximum allowed difference between the model normal and the given axis in radians. </td></tr>
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  </dd>
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<div class="fragment"><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;{ <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#ac40b9cd690894f581d507dabbeb89c3c">eps_angle_</a> = ea; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a47c5241af3824ee197e3a9c1b89806c4">&#9670;&nbsp;</a></span>setMaxIterations()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setMaxIterations </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>max_iterations</em></td><td>)</td>
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<p>Set the maximum number of iterations before giving up. </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">max_iterations</td><td>the maximum number of iterations the sample consensus method will run </td></tr>
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<div class="fragment"><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;{ <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af99af32608e22a07bc0d8e0cf92d20fb">max_iterations_</a> = max_iterations; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a1f01af4b5cc22e916c4facc145bc9297">&#9670;&nbsp;</a></span>setMethodType()</h2>

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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setMethodType </td>
          <td>(</td>
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          <td class="paramname"><em>method</em></td><td>)</td>
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<p>The type of sample consensus method to use (user given parameter). </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">method</td><td>the method type (check <em><a class="el" href="method__types_8h_source.html">method_types.h</a></em>) </td></tr>
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  </dd>
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<div class="fragment"><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;{ <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#af22b2d5e01d6c549b5d7c14caaa28539">method_type_</a> = method; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#aaf3488729fa23a602cc0ef2e9480c5f5">&#9670;&nbsp;</a></span>setModelType()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setModelType </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>model</em></td><td>)</td>
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<p>The type of model to use (user given parameter). </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">model</td><td>the model type (check <em><a class="el" href="model__types_8h_source.html">model_types.h</a></em>) </td></tr>
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  </dd>
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<div class="fragment"><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;{ <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a444d6de9dcabbe20ad1a152bea746dab">model_type_</a> = model; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#acd7cb38442e52a3df81bc0fd28c07646">&#9670;&nbsp;</a></span>setOptimizeCoefficients()</h2>

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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setOptimizeCoefficients </td>
          <td>(</td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>optimize</em></td><td>)</td>
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<p>Set to true if a coefficient refinement is required. </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">optimize</td><td>true for enabling model coefficient refinement, false otherwise </td></tr>
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<div class="fragment"><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;{ <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#abe8902ffbc5fd352e38c08f8b9b3601b">optimize_coefficients_</a> = optimize; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a6c436f52078056b626aba0c36b819235">&#9670;&nbsp;</a></span>setProbability()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setProbability </td>
          <td>(</td>
          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>probability</em></td><td>)</td>
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<p>Set the probability of choosing at least one sample free from outliers. </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">probability</td><td>the model fitting probability </td></tr>
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<div class="fragment"><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;{ <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a29d30627faa3b625702191de4fb3da9b">probability_</a> = probability; }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a18ba57a99fbbcb0f2d0500b0d21579d2">&#9670;&nbsp;</a></span>setRadiusLimits()</h2>

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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setRadiusLimits </td>
          <td>(</td>
          <td class="paramtype">const double &amp;&#160;</td>
          <td class="paramname"><em>min_radius</em>, </td>
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          <td class="paramname"><em>max_radius</em>&#160;</td>
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<p>Set the minimum and maximum allowable radius limits for the model (applicable to models that estimate a radius) </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">min_radius</td><td>the minimum radius model </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">max_radius</td><td>the maximum radius model </td></tr>
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<div class="fragment"><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;      {</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;        <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a873c817931f20ad93869526584c67e22">radius_min_</a> = min_radius;</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;        radius_max_ = max_radius;</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ac8a399afca15a3e2dcbf491a8de3e2f1">&#9670;&nbsp;</a></span>setSamplesMaxDist()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_s_a_c_segmentation.html">pcl::SACSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setSamplesMaxDist </td>
          <td>(</td>
          <td class="paramtype">const double &amp;&#160;</td>
          <td class="paramname"><em>radius</em>, </td>
        </tr>
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          <td class="paramkey"></td>
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          <td class="paramtype">SearchPtr&#160;</td>
          <td class="paramname"><em>search</em>&#160;</td>
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          <td>)</td>
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<p>Set the maximum distance allowed when drawing random samples </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">radius</td><td>the maximum distance (L2 norm) </td></tr>
    <tr><td class="paramdir"></td><td class="paramname">search</td><td></td></tr>
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<div class="fragment"><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;      {</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;        <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#acfefedfd77b047203d70b9a73c1cbec8">samples_radius_</a> = radius;</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;        <a class="code" href="classpcl_1_1_s_a_c_segmentation.html#a8decd0cf3fa904cd8d2530879a7c726b">samples_radius_search_</a> = search;</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;      }</div>
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<hr/>该类的文档由以下文件生成:<ul>
<li>segmentation/include/pcl/segmentation/<a class="el" href="sac__segmentation_8h_source.html">sac_segmentation.h</a></li>
<li>segmentation/include/pcl/segmentation/impl/<a class="el" href="sac__segmentation_8hpp_source.html">sac_segmentation.hpp</a></li>
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